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The Computation of Average Optimal Policies in Denumerable State Markov Decision Chains

Part of: Markov processes Stochastic systems and control Operations research and management science

Published online by Cambridge University Press:  01 July 2016

Linn I. Sennott
Linn I. Sennott*
Affiliation:
Illinois State University
*
∗ Postal address: Department of Mathematics 4520, Illinois State University, Normal, IL 61790-4520, USA. sennott@math.ilstu.edu
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Abstract

This paper studies the expected average cost control problem for discrete-time Markov decision processes with denumerably infinite state spaces. A sequence of finite state space truncations is defined such that the average costs and average optimal policies in the sequence converge to the optimal average cost and an optimal policy in the original process. The theory is illustrated with several examples from the control of discrete-time queueing systems. Numerical results are discussed.

Keywords

CONTROLLED MARKOV PROCESSESCOMPUTATION OF AVERAGE COST OPTIMAL STATIONARY POLICIESCONTROL OF DISCRETE-TIME QUEUES

MSC classification

Primary: 93E20: Optimal stochastic control
Secondary: 90B22: Queues and service 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 29 , Issue 1 , March 1997 , pp. 114 - 137
Copyright
Copyright © Applied Probability Trust 1997 

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Footnotes

This material is based upon work supported by the National Science Foundation under Grant No. ECS-9309154.

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